Societal Implications of Enabling Technologies:
A Review of the International Literature
Prepared by Dr Sarah Davies, University of Copenhagen, Denmark
Commissioned under the National Enabling Technologies Strategies to set the scene for a STEP engagement of the same name.
This literature review considers the societal implications of enabling technologies. By reviewing the international literature in this area – including the grey literature of policy reports, thinktank studies and government documents as well as academic research – it sets the scene for results from a recent deliberative workshop considering Societal Implications of Enabling Technologies, held under the Science & Technology Engagement Pathways (STEP) framework. It concludes with a brief discussion of how this literature prefigures or contrasts with findings from the STEP workshop.
In what follows a number of specific areas are explored. First, the scene is set through consideration of the wider dynamics shaping technological development and societal impacts. Second, the ways in which publics have assessed the implications of new and emerging technologies are discussed through a focus on quantitative and qualitative studies of public opinion and perceptions of technology. Third, deliberative and participatory processes that have taken place internationally in this area are reviewed. Fourth, specialist accounts regarding the societal implications of technologies – such as those from ethicists, philosophers and policy makers – are covered. The final section concludes the review by summarising the key themes that emerge from this literature as a whole, noting key divergences that appear across different national contexts and between different technological application areas, and making a brief comparison with the STEP workshop.
The scope of this review is thus the societal implications of enabling technologies. ‘Societal implications’ are understood broadly, as including all issues around how technologies affect people individually and collectively, such as effects on work, lifestyles, relationships, and culture, and on values such as equity, privacy and choice. ‘Enabling technologies’ is a relatively specialised term that covers “the bio- and nano-enabling technologies, converging with information technologies and cognitive science” (National Enabling Technologies Strategy Expert Forum 2012, 1). In this review, nanotechnology, biotechnology and synthetic biology are a particular focus. There is also an overlap with terms such as ‘converging technologies’, ‘emerging technologies’ and ‘platform technologies’, and these have been included in the literature search. A full list of the search terms used can be found in the Appendix, along with a complete bibliography.
The context for this discussion, and of the STEP workshop itself, is the increasingly complex nature of contemporary technological society. A number of scholars have argued that the times we live in are unprecedented in the degree to which trends such as globalisation, cosmopolitanism, and an ever-faster increase in the complexity of technological systems are presenting new challenges for democracy on both the macro and micro levels (see, for instance, Appiah et al 2007; Giddens 1990; Weinberg 1972). Ulrich Beck has suggested that we now live in a ‘risk society’, in which the management and distribution of long-term, unanticipated and often invisible risks has replaced that of wealth (Beck 1992). Others have argued that developed world nations are transitioning from ‘Mode 1’ to ‘Mode 2’ societies, where science and society are more closely intertwined through new processes of innovation (Gibbons et al 1994; Nowotny et al 2001); or that scientific research is in the process of moving into a ‘postnormal’ mode better able to manage uncertainties and take a problem-solving approach (Funcowitz and Ravetz 1993; Sardar 2010). Many of these accounts suggest that new stakeholders must be integrated into – or at least considered by – decision making processes, and thus that uncertainties can best be managed by the “inclusion of an ever-growing set of legitimate participants in the process of quality assurance of … scientific inputs” (Funcowitz and Ravetz 1993, 752).
The economic structure of these developments has also been a focus of attention. Innovation studies has, as a discipline, tended to assume a linear model of technological development (derived originally from the work of Joseph Schumpeter): basic science discovers natural phenomena, basic technology works out applications from this knowledge, and new high-tech industries develop to commercialise these applications, bringing economic growth to a particular region or country (Betz 2011; Fagerberg and Verspagen 2009). This one-way model, though still influential, has been widely challenged: it ignores the ways in which society itself affects the innovation process, and underestimates the degree to which technologies evolve and are finessed throughout their development and subsequent use (Joly and Kaufman 2008; Kline and Rosenburg 1986). Recent research has suggested that successfully transferring knowledge between university and industrial contexts – sites which operate under different assumptions, and which utilise rather different kinds of knowledge – requires a high degree of specialist support, such as that provided by technology transfer offices or specialised university spin-outs (Fontes 2005; Guena and Muscio 2009). Geography (such as the existence of ‘innovation clusters’) also appears to be a key factor in whether knowledge transfer, successful product development, and economic growth occurs (Tödtling and Trippl 2005). This complexity means that economic and societal impacts of new technologies remain fundamentally difficult to forecast (Porter et al 2011). Despite this, strong expectations remain that emerging technologies will bring about economic impacts, and that these will be positive (Lloyds 2007; Lundvall and Borrás 2005; National Enabling Technologies Strategy Expert Forum 2012; Silberglitt et al 2006), with effects such as increased efficiency in production processes as well as the development of new commercial products (Seear et al 2009).
Historically, rapid technological development has brought concerns regarding workforce impacts and, in particular, the possibility of unemployment through technological ‘replacement’ of workers (Carter 1981; Jones 1990; Woirol 1996). In the most recent case of ICTs, for instance, there were concerns regarding unemployment resulting from automation, outsourcing, and increased casualisation (Freeman and Soete 1994; Noble 1995). Labour force restructuring has certainly taken place over the last decades, but, as Baldoz et al note (2001), it is difficult to disentangle the role of technological change from that of economic and political transition within this. And thus far there has been surprisingly little attention paid to the potential of enabling technologies to disrupt employment structure and levels (Invernizzi 2011). It certainly seems likely that there will be impacts in terms of workplace safety – for instance in the context of nanotechnology, where there are specific health concerns regarding the effects of nanoparticles on workers (ibid). Similarly, there are calls for workforces with particular technical skillsets, and concern over the potential for employment disruption as old technologies are replaced by new ones (Freeman and Shukla 2008; Invernizzi 2011; Van Horn and Fichtner 2008).
The broader context of the development of enabling technologies is thus one of profound uncertainty: their economic, technical, and societal impacts are all difficult to forecast (Sutcliffe and Hodgson 2006). A number of strategies have been used in an attempt to manage these uncertainties, including roadmapping and technology assessment, stepped decision making such as STAGE gating, and early stakeholder engagement (Fleischer et al 2005; Funcowitz and Ravetz 1993; Macnaghten and Owen 2011). Technology assessment (TA), in particular, has a long history in North America and Europe, and has been used as a means of integrating scientific and societal analysis (Fleischer et al 2005; Guston and Sarewitz 2002). Forms of TA such as constructive TA (CTA) or real time TA (RTTA), for instance, bring an explicit focus on multi-stakeholder deliberation alongside scientific roadmapping and risk assessment (ibid; Hellström 2003; Sclove 2010). Most recently, the notion of ‘responsible innovation’ has been used as a means of drawing together approaches such as public engagement, the use of soft law, and continual assessment of impacts in order to make robust decisions on technological development (DG Research 2011; Owen and Goldberg 2010; Stilgoe 2012).